The rationales are as follows. First, before the release time of a given event, invitation behaviors of source users are unknown, and hence corresponding ego networks fail to be extracted. Second, even though the ego networks may be constructed by exploiting source behaviors from historical events, however, the invitation behaviors are only triggered by a small number of source users, which renders a much sparser ego network with many singleton \({\mathsf {CCs}}\)  (i.e., one inside member) [1]} and makes less group information revealed.
For example, Figure REF  shows the distributions of averaged \({\mathsf {CC}}\)  size of invited target users on FPS dataset, in which averaged \({\mathsf {CC}}\)  size of a target \(v_t\)  equals the fraction of the number of all (or inviting) source neighbors over \(\#\)\({\mathsf {CC}}\)  of \(v_t\) . Specifically, by using the inviting source nodes, the resulting distribution in blue triangles is highly-skewed, where singletons are over 99%. In contrast, by using all source neighbors, the results in red circles show an improvement w.r.t the average size, which reveals more group information.
